2. in American football, the likelihood of a successful field goal declines with increasing distance. The following data were collected for one week from games played by teams in the National Football League. Yards No X Made? 1 = Yes; 0 = Toniwo 34 20 51 32 51 29 19 37 43 47 24 31 41 22 26 (Continued, (Continued) Made? 1 = Yes; 0 = No %3D Yards X %3D 34 1. 41 24 39 43 Use logistic regression to determine how the odds of making a field goal /change as distance increases. Use the results to draw a graph depicting how the predicted probability of making a field goal changes with distance. (c) In the waning seconds of SuperBowl XXV, Scott Norwood missed a 47-yard field goal that would have carried the Buffalo Bills to victory over the New York Giants, Use your model to predict the likelihood that a kicker is successful at a 47-yard field goal attempt. Did Norwood really deserve the criticism he received for missing the attempt?

2. in American football, the likelihood of a successful field goal declines with increasing distance. The following data were collected for one week from games played by teams in the National Football League. Yards No X Made? 1 = Yes; 0 = Toniwo 34 20 51 32 51 29 19 37 43 47 24 31 41 22 26 (Continued, (Continued) Made? 1 = Yes; 0 = No %3D Yards X %3D 34 1. 41 24 39 43 Use logistic regression to determine how the odds of making a field goal /change as distance increases. Use the results to draw a graph depicting how the predicted probability of making a field goal changes with distance. (c) In the waning seconds of SuperBowl XXV, Scott Norwood missed a 47-yard field goal that would have carried the Buffalo Bills to victory over the New York Giants, Use your model to predict the likelihood that a kicker is successful at a 47-yard field goal attempt. Did Norwood really deserve the criticism he received for missing the attempt?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
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The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.8 8.4 7.2 3.6 2.7 2.7 2.2 0.7 yy 13.8 11.5 10 7.2 6.4 6.1 6.2 4.4 xx = thousands of automatic weaponsyy = murders per 100,000 residents This data can be modeled by the equation y=0.84x+4.06.y=0.84x+4.06. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 4.9 thousand automatic weapons?Answer = Round to 3 decimal places.
The data from the table below gives a regression that is a) reliable. b) unreliable. c) unable to determine the reliability.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. X Y 11.4 8.6 7.2 3.6 13.5 11.3 10.1 x = thousands of automatic weapons y = murders per 100,000 residents 2.4 2.8 2.3 0.9 7 6.1 6.6 5.9 4.7 This data can be modeled by the equation y = 0.83x +4.06. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. Answer = A) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 11 thousand automatic weapons? Round to 3 decimal places.
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Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. Xx Y 11.8 14.1 11.5 9.8 7.1 6.2 6.5 x = thousands of automatic weapons y = murders per 100,000 residents 8.6 7 3.3 2.5 2.5 2.6 6 This data can be modeled by the equation y = 0.85x +4.07. Use this equation to answer the following; Answer decimal places. = A) How many murders per 100,000 residents can be expected in a state with 1.2 thousand automatic weapons? Answer = decimal places. Round to 3 0.9 4.7 B) How many murders per 100,000 residents can be expected in a state with 8.5 thousand automatic weapons? Round to 3
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = + x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.2 6.9 3.6 2.5 2.8 2.6 0.4 14 11.3 10.2 7.4 6.5 6.3 5.9 4.5 x thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equationý = 0.85x + 4.12. Use this equation to answer the following; %D A) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons? Answer = Nearest whole value. B) How many murders per 100,000 residents can be expected in a state with 1 thousand automatic weapons? Nearest whole value Answer =